Micron technology, inc. (20240161169). USING MACHINE LEARNING TO IDENTIFY MEMORY COMPATIBILITY simplified abstract
Contents
- 1 USING MACHINE LEARNING TO IDENTIFY MEMORY COMPATIBILITY
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 USING MACHINE LEARNING TO IDENTIFY MEMORY COMPATIBILITY - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
USING MACHINE LEARNING TO IDENTIFY MEMORY COMPATIBILITY
Organization Name
Inventor(s)
USING MACHINE LEARNING TO IDENTIFY MEMORY COMPATIBILITY - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240161169 titled 'USING MACHINE LEARNING TO IDENTIFY MEMORY COMPATIBILITY
Simplified Explanation
The patent application describes a device that uses machine learning models to recommend memory types based on the device type and configuration.
- The device obtains input identifying a device type and information indicating a configuration associated with the device type.
- Using machine learning models trained for different memory types, the device determines compatibilities between memory types and the device type based on the configuration.
- The device then recommends one or more memory types for the device type based on these compatibilities.
- Finally, the device transmits an indication of the recommended memory types.
Potential Applications
This technology could be applied in various industries such as electronics manufacturing, computer hardware development, and consumer electronics.
Problems Solved
This technology helps in optimizing memory selection for different device types, ensuring better performance and compatibility.
Benefits
The benefits of this technology include improved device performance, enhanced user experience, and reduced compatibility issues.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of smart devices where memory optimization is crucial for overall performance.
Possible Prior Art
One possible prior art could be memory optimization software that suggests memory upgrades based on system configurations, but the use of machine learning models for memory type recommendations may be a novel approach.
What are the specific machine learning models used in this technology?
The specific machine learning models used in this technology are not mentioned in the abstract. Further details on the types of models and their training methods would provide a clearer understanding of the innovation.
How does the device handle conflicting recommendations from different machine learning models?
The abstract does not mention how the device resolves conflicting recommendations from different machine learning models. Understanding the decision-making process in such scenarios would be essential for implementing this technology effectively.
Original Abstract Submitted
in some implementations, a device may obtain an input that identifies a device type. the device may obtain, based on the input, information indicating a configuration associated with the device type. the device may determine, using a plurality of machine learning models respectively associated with a plurality of memory types, compatibilities between the plurality of memory types and the device type based on the configuration associated with the device type. each of the plurality of machine learning models may be trained to determine a compatibility of a respective memory type, of the plurality of memory types, with a given configuration. the device may determine a recommendation of one or more memory types for the device type based on the compatibilities between the plurality of memory types and the device type. the device may transmit an indication of the recommendation of the one or more memory types.
